Workspace Optimization Techniques to Improve Prediction of Human Motion During Human-Robot Collaboration
Understanding human intentions is critical for safe and effective human-robot collaboration. While state of the art methods for human goal prediction utilize learned models to account for the uncertainty of human motion data, that data is inherently stochastic and high variance, hindering those mode...
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| Veröffentlicht in: | 2024 19th ACM/IEEE International Conference on Human-Robot Interaction (HRI) S. 743 - 751 |
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11.03.2024
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| Abstract | Understanding human intentions is critical for safe and effective human-robot collaboration. While state of the art methods for human goal prediction utilize learned models to account for the uncertainty of human motion data, that data is inherently stochastic and high variance, hindering those models' utility for interactions requiring coordination, including safety-critical or close-proximity tasks. Our key insight is that robot teammates can deliberately configure shared workspaces prior to interaction in order to reduce the variance in human motion, realizing classifier-agnostic improvements in goal prediction. In this work, we present an algorithmic approach for a robot to arrange physical objects and project "virtual obstacles" using augmented reality in shared human-robot workspaces, optimizing for human legibility over a given set of tasks. We compare our approach against other workspace arrangement strategies using two human-subjects studies, one in a virtual 2D navigation domain and the other in a live tabletop manipulation domain involving a robotic manipulator arm. We evaluate the accuracy of human motion prediction models learned from each condition, demonstrating that our workspace optimization technique with virtual obstacles leads to higher robot prediction accuracy using less training data.CCS CONCEPTS* Computing methodologies → Robotic planning; Planning under uncertainty; * Human-centered computing → Mixed / augmented reality. |
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| AbstractList | Understanding human intentions is critical for safe and effective human-robot collaboration. While state of the art methods for human goal prediction utilize learned models to account for the uncertainty of human motion data, that data is inherently stochastic and high variance, hindering those models' utility for interactions requiring coordination, including safety-critical or close-proximity tasks. Our key insight is that robot teammates can deliberately configure shared workspaces prior to interaction in order to reduce the variance in human motion, realizing classifier-agnostic improvements in goal prediction. In this work, we present an algorithmic approach for a robot to arrange physical objects and project "virtual obstacles" using augmented reality in shared human-robot workspaces, optimizing for human legibility over a given set of tasks. We compare our approach against other workspace arrangement strategies using two human-subjects studies, one in a virtual 2D navigation domain and the other in a live tabletop manipulation domain involving a robotic manipulator arm. We evaluate the accuracy of human motion prediction models learned from each condition, demonstrating that our workspace optimization technique with virtual obstacles leads to higher robot prediction accuracy using less training data.CCS CONCEPTS* Computing methodologies → Robotic planning; Planning under uncertainty; * Human-centered computing → Mixed / augmented reality. |
| Author | Roncone, Alessandro Hayes, Bradley Tung, Yi-Shiuan Luebbers, Matthew B. |
| Author_xml | – sequence: 1 givenname: Yi-Shiuan surname: Tung fullname: Tung, Yi-Shiuan email: yi-shiuan.tung@colorado.edu organization: University of Colorado Boulder,Boulder,USA – sequence: 2 givenname: Matthew B. surname: Luebbers fullname: Luebbers, Matthew B. email: matthew.luebbers@colorado.edu organization: University of Colorado Boulder,Boulder,USA – sequence: 3 givenname: Alessandro surname: Roncone fullname: Roncone, Alessandro email: alessandro.roncone@colorado.edu organization: University of Colorado Boulder,Boulder,USA – sequence: 4 givenname: Bradley surname: Hayes fullname: Hayes, Bradley email: bradley.hayes@colorado.edu organization: University of Colorado Boulder,Boulder,USA |
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| Snippet | Understanding human intentions is critical for safe and effective human-robot collaboration. While state of the art methods for human goal prediction utilize... |
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| SubjectTerms | Accuracy Adaptation models augmented reality Collaboration environment adaptation human-robot collaboration legibility motion prediction Prediction algorithms Predictive models Robot kinematics Uncertainty |
| Title | Workspace Optimization Techniques to Improve Prediction of Human Motion During Human-Robot Collaboration |
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